1. Field
The present disclosure relates to a system and method for hybrid vehicle system loss learning and more particularly to a system and method of using hybrid powertrain signals to detect the total system losses and perform long-term learning of the system losses to improve control accuracy and optimize fuel efficiency.
2. Description of the Related Art
Hybrid vehicles have become more popular because of rising fuel costs. The improved fuel efficiency of hybrid vehicles over conventional internal combustion engine (ICE) vehicles is particularly attractive to consumers. As hybrid vehicles become more commonplace, consumers may have raised expectations for fuel efficiency. Consumers may also become aware of the diminishing fuel efficiency of aging vehicles.
A hybrid vehicle utilizes both an engine, such as an internal combustion engine (ICE), and an electric motor, such as a motor-generator, to provide power to the wheels. When there is a power request from the driver, the hybrid system determines how to deliver that power to the wheels, using a combination of the engine and the electric motor. However, the power commanded by the hybrid system may not equal the power actually delivered to the wheels. The difference in power is a system loss, or loss. Due to such losses, the hybrid system's power calculations may be off, reducing control accuracy and negatively affecting fuel efficiency.
Current hybrid systems may have predetermined calculations, which may be stored in a map, to account for system losses. However, the predetermined calculations may be determined from bench testing results. Therefore, the predetermined calculations fail to account for variations between the vehicles themselves, as well as aging of the hardware. The predetermined calculations may not always provide the optimal control to account for losses.
Thus, there is a need for a system and method directed to long-term learning of an individual hybrid vehicle's system losses to improve control accuracy and optimize fuel efficiency.